Back to Community
How to filter based on more than one Morningstar Fundamental "Grade"?

I want to play around with the Morningstar fundamental "grades" for stocks. I am successful with selecting a single grade (= "A") but not multiple (= "A or B"). Can I use a regular expression (regex) in this case? Something like (= "A|B")?

    fundamental_df = get_fundamentals(  
        query(  
            # put your query in here by typing "fundamentals."  
            fundamentals.asset_classification.growth_score, # 0 to 100  
            fundamentals.asset_classification.value_score, # 0 to 100  
            fundamentals.asset_classification.growth_grade, # A,B,C,D,E,F  
            fundamentals.asset_classification.financial_health_grade, # A,B,C,D,E,F  
            fundamentals.asset_classification.profitability_grade # A,B,C,D,E,F  
         )  
        .filter(fundamentals.valuation.market_cap != None)  
        .filter(fundamentals.valuation.shares_outstanding != None)  
#       .filter(fundamentals.asset_classification.growth_score > 90)  
#        .filter(fundamentals.asset_classification.value_score > 90)  
        .filter(fundamentals.asset_classification.growth_grade == "A")  
#        .filter(fundamentals.asset_classification.financial_health_grade == "A")  
#        .filter(fundamentals.asset_classification.profitability_grade == "A")  
        .order_by(fundamentals.valuation.market_cap.desc())  
        .limit(num_stocks)  
    )  
8 responses

Tristan.. do A.. B...and C hav the same... market cap? If not wat is the market cap to be... classified... as F. tnxs

.filter((fundamentals.asset_classification.growth_grade == "A") or (fundamentals.asset_classification.growth_grade == "B"))

Thanks Max, I did what you said but it seemed to stop obeying the limit operation...

    num_stocks = 500

    fundamental_df = get_fundamentals(  
        query(  
            # put your query in here by typing "fundamentals."  
            fundamentals.asset_classification.growth_score, # 0 to 100  
            fundamentals.asset_classification.value_score, # 0 to 100  
            fundamentals.asset_classification.growth_grade, # A,B,C,D,E,F  
            fundamentals.asset_classification.financial_health_grade, # A,B,C,D,E,F  
            fundamentals.asset_classification.profitability_grade # A,B,C,D,E,F  
         )  
        .filter(fundamentals.valuation.market_cap != None)  
        .filter(fundamentals.valuation.shares_outstanding != None)  
#       .filter(fundamentals.asset_classification.growth_score < 50)  
#        .filter(fundamentals.asset_classification.value_score > 70)  
#        .filter(fundamentals.asset_classification.growth_grade == "D")  
#        .filter((fundamentals.asset_classification.growth_grade == "A") or (fundamentals.asset_classification.growth_grade == "B"))  
        .filter(fundamentals.asset_classification.financial_health_grade == "D") or (fundamentals.asset_classification.financial_health_grade == "C")  
        .filter(fundamentals.asset_classification.profitability_grade == "D") or (fundamentals.asset_classification.profitability_grade == "C")  
        .order_by(fundamentals.valuation.market_cap.desc())  
        .limit(num_stocks)  
    )  

This is the error I get:

UniverseExceedsSizeLimit: 0029 Universe size 658 exceeds limit of 500
for minute bars.

Tristan, can you set the num_stocks variable you use in .limit() to 500 or lower? That should fix this.

Disclaimer

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.

I have it set to 500 (as you can see above). Do you think my # comments might be messing with this query?

Ah, somehow I missed that. I think what might be messing up the query is the number of filters you are placing. There may be a bug where, after a certain number of filters, the remaining filters get ignored, but I am not sure. Could you try to remove some of the filters? The workaround is to query, then filter later. I don't think the comments should be affecting the query. Generally, I would start from a barebones query that works. Then build up the query to find the failure point. Let me know if that works.

the "or" you are using is not valid syntax for creating a query with SQLAlchemy. I'm guessing the space was causing the latter portions of the query (including the ordering and the limiting) to be ignored.

I replaced your ORs with an SQL "in" query an it seems to work for me:

def before_trading_start(context):  
    num_stocks = 500

    fundamental_df = get_fundamentals(  
        query(  
            # put your query in here by typing "fundamentals."  
            fundamentals.asset_classification.growth_score, # 0 to 100  
            fundamentals.asset_classification.value_score, # 0 to 100  
            fundamentals.asset_classification.growth_grade, # A,B,C,D,E,F  
            fundamentals.asset_classification.financial_health_grade, # A,B,C,D,E,F  
            fundamentals.asset_classification.profitability_grade # A,B,C,D,E,F  
         )  
        .filter(fundamentals.valuation.market_cap != None)  
        .filter(fundamentals.valuation.shares_outstanding != None)  
        .filter(fundamentals.asset_classification.financial_health_grade.in_(["D", "C"]))  
        .filter(fundamentals.asset_classification.profitability_grade.in_(["D","C"]))  
        .order_by(fundamentals.valuation.market_cap.desc())  
        .limit(num_stocks)  
    )  
Disclaimer

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.

I should note, these docs from SQLAlchemy are useful in these situations: http://docs.sqlalchemy.org/en/rel_0_9/orm/tutorial.html#common-filter-operators